Arnab Chanda1,2, Subhodip Chatterjee2. 1. Centre for Biomedical Engineering, IIT, Delhi, India. 2. Department of Biomedical Engineering, AIIMS, Delhi, India.
Abstract
BACKGROUND: Sedentary lifestyle and work from home schedules due to the ongoing COVID-19 pandemic in 2020 have caused a significant rise in obesity across adults. With limited visits to the doctors during this period to avoid possible infections, there is currently no way to measure or track obesity. METHODS: We reviewed the literature on relationships between obesity and facial features, in white, black, hispanic-latino, and Korean populations and validated them against a cohort of Indian participants (n = 106). The body mass index (BMI) and waist-to-hip ratio (WHR) were obtained using anthropometric measurements, and body fat mass (BFM), percentage body fat (PBF), and visceral fat area (VFA) were measured using body composition analysis. Facial pictures were also collected and processed to characterize facial geometry. Regression analysis was conducted to determine correlations between body fat parameters and facial model parameters. RESULTS: Lower facial geometry was highly correlated with BMI (R 2 = 0.77) followed by PBF (R 2 = 0.72), VFA (R 2 = 0.65), WHR (R 2 = 0.60), BFM (R 2 = 0.59), and weight (R 2 = 0.54). CONCLUSIONS: The ability to predict obesity using facial images through mobile application or telemedicine can help with early diagnosis and timely medical intervention for people with obesity during the pandemic.
BACKGROUND: Sedentary lifestyle and work from home schedules due to the ongoing COVID-19 pandemic in 2020 have caused a significant rise in obesity across adults. With limited visits to the doctors during this period to avoid possible infections, there is currently no way to measure or track obesity. METHODS: We reviewed the literature on relationships between obesity and facial features, in white, black, hispanic-latino, and Korean populations and validated them against a cohort of Indian participants (n = 106). The body mass index (BMI) and waist-to-hip ratio (WHR) were obtained using anthropometric measurements, and body fat mass (BFM), percentage body fat (PBF), and visceral fat area (VFA) were measured using body composition analysis. Facial pictures were also collected and processed to characterize facial geometry. Regression analysis was conducted to determine correlations between body fat parameters and facial model parameters. RESULTS: Lower facial geometry was highly correlated with BMI (R 2 = 0.77) followed by PBF (R 2 = 0.72), VFA (R 2 = 0.65), WHR (R 2 = 0.60), BFM (R 2 = 0.59), and weight (R 2 = 0.54). CONCLUSIONS: The ability to predict obesity using facial images through mobile application or telemedicine can help with early diagnosis and timely medical intervention for people with obesity during the pandemic.
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